1 Introduction

For this project, we’ll be exploring the data about Covid-19 Vaccinations gathered by “Our World In Data”.

1.1 Goals

  • To show the total number of people vaccinated worldwide:
    • At the current time
    • Visualizing the total over time
  • To show the number of countries reporting milestones over time:
  • To show the percentage of the population vaccinated by each country:
    • Listing the current results in a sortable and searchable table
    • Visualizing the current results in a choropleth map
    • Visualizing the current results in a dot density map
    • Visualizing the results for the top n countries over time in an animated bar chart
  • To visualize the vaccination progress on a choropleth map
  • To explore the potential relationship between various economic measurements and vaccination rates, for example:
    • GDP and / or GDP per capita
    • World Bank Income Income Groups

1.2 Libraries

2 Loading the Data

2.1 Downloading the data

We’ll be using the Data on COVID-19 (coronavirus) vaccinations from Our World in Data. For details of the data set, see the Further Information section at the end of this document.

The data is updated daily, so if the file should be downloaded if it either does not exist locally, or has not been modified for more than 1 day.

source_location <- 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv'
local_file_location <- 'Data/covid_Vaccination_data.csv'
result <- download.file(source_location, destfile = local_file_location, 
                          method="wininet", quiet = TRUE)

2.2 Reading the Local Data

Let’s start by reading in the data from the local CSV file, and having a look at the structure of the data:

vaccination_data <- read.csv(local_file_location)
str(vaccination_data, vec.len = 3)
## 'data.frame':    9818 obs. of  12 variables:
##  $ location                           : chr  "Afghanistan" "Afghanistan" "Afghanistan" ...
##  $ iso_code                           : chr  "AFG" "AFG" "AFG" ...
##  $ date                               : chr  "2021-02-22" "2021-02-23" "2021-02-24" ...
##  $ total_vaccinations                 : int  0 NA NA NA NA NA 8200 NA ...
##  $ people_vaccinated                  : int  0 NA NA NA NA NA 8200 NA ...
##  $ people_fully_vaccinated            : int  NA NA NA NA NA NA NA NA ...
##  $ daily_vaccinations_raw             : int  NA NA NA NA NA NA NA NA ...
##  $ daily_vaccinations                 : int  NA 1367 1367 1367 1367 1367 1367 1580 ...
##  $ total_vaccinations_per_hundred     : num  0 NA NA NA NA NA 0.02 NA ...
##  $ people_vaccinated_per_hundred      : num  0 NA NA NA NA NA 0.02 NA ...
##  $ people_fully_vaccinated_per_hundred: num  NA NA NA NA NA NA NA NA ...
##  $ daily_vaccinations_per_million     : int  NA 35 35 35 35 35 35 41 ...

Our World in Data update this csv file once per day, with one observation (row) added each day for each country which has updated vaccination statistics that day.

The first three variables (columns) are fairly straightforward to understand:

  • location: name of the country or other location to which the results apply
  • iso-code: a unique identifier for the location.This is based on ISO-3166 standards **link and will allow us to link this data to other data sets
  • date: the date that the information was recorded

Many of the vaccines require more than one dose to be fully effective, so several different variables are being tracked:

  • total_vaccinations: total number of doses administered. If a person receives one dose of the vaccine, this metric goes up by 1. If they receive a second dose, it goes up by 1 again.
  • people_vaccinated: total number of people who have received at least one vaccine dose. If a person receives the first dose of a 2-dose vaccine, this metric goes up by 1. If they receive the second dose, the metric stays the same.
  • people_fully_vaccinated: total number of people who have received all doses prescribed by the vaccination protocol. If a person receives the first dose of a 2-dose vaccine, this metric stays the same. If they receive the second dose, the metric goes up by 1.

There are also separate measures for daily_vaccinations_raw and daily_vaccinations. The raw figure is ‘provided for data checks and transparency’ and Our World in Data recommend using daily_vaccinations instead.

3 Cleaning the data

3.1 Removing Unwanted columns

We won’t be using the daily data, so we can remove those columns:

cleaned_vacc_data <- vaccination_data %>%
  select(!starts_with('daily'))
str(cleaned_vacc_data, vec.len = 3)
## 'data.frame':    9818 obs. of  9 variables:
##  $ location                           : chr  "Afghanistan" "Afghanistan" "Afghanistan" ...
##  $ iso_code                           : chr  "AFG" "AFG" "AFG" ...
##  $ date                               : chr  "2021-02-22" "2021-02-23" "2021-02-24" ...
##  $ total_vaccinations                 : int  0 NA NA NA NA NA 8200 NA ...
##  $ people_vaccinated                  : int  0 NA NA NA NA NA 8200 NA ...
##  $ people_fully_vaccinated            : int  NA NA NA NA NA NA NA NA ...
##  $ total_vaccinations_per_hundred     : num  0 NA NA NA NA NA 0.02 NA ...
##  $ people_vaccinated_per_hundred      : num  0 NA NA NA NA NA 0.02 NA ...
##  $ people_fully_vaccinated_per_hundred: num  NA NA NA NA NA NA NA NA ...

3.2 Date format

The date variable is being stored in character format, so it should to be converted to a Date.

cleaned_vacc_data %<>%
  mutate(date = as.Date(date, format= "%Y-%m-%d"))
cleaned_vacc_data

3.3 Missing Dates

Browsing through the data, we can see that different countries started reporting their vaccinations at different times, and some have gaps between records.

Let’s summarise the first and last date when each country recorded vaccination numbers, along with the total number of reports made in that time span:

vacc_report_dates_by_country <- 
  cleaned_vacc_data %>%
  group_by(location) %>%
  summarise(first_vacc_record = min(date),
            last_vacc_record = max(date),
            date_span = difftime(last_vacc_record, 
                                 first_vacc_record, 
                                 units = 'days') + 1,
            vacc_report_count = n_distinct(date))
vacc_report_dates_by_country

It’s pretty easy to see that the countries have different dates for their first and last record, but for most countries the date_span and vacc_report_count is the same, meaning that there is a record for every day during that time. Sometimes there are exceptions, and there will be days with missing data:

vacc_report_dates_by_country %>%
  filter(difftime(last_vacc_record, 
                  first_vacc_record, 
                  units = 'days')
         - vacc_report_count > 0)

We want to know the total figures for each country on any given date, so we’ll need to generate records for the missing dates. We can do this using the complete() function from dplyr:

complete_vacc_data <-
  cleaned_vacc_data %>% 
  complete(location, date = seq.Date(min(date), max(date), by = 'day') )
complete_vacc_data
summary(complete_vacc_data)
##    location              date              iso_code         total_vaccinations 
##  Length:18252       Min.   :2020-12-13   Length:18252       Min.   :        0  
##  Class :character   1st Qu.:2021-01-08   Class :character   1st Qu.:    51678  
##  Mode  :character   Median :2021-02-04   Mode  :character   Median :   402115  
##                     Mean   :2021-02-04                      Mean   :  8927709  
##                     3rd Qu.:2021-03-03                      3rd Qu.:  2541835  
##                     Max.   :2021-03-30                      Max.   :577923364  
##                                                             NA's   :12032      
##  people_vaccinated   people_fully_vaccinated total_vaccinations_per_hundred
##  Min.   :        0   Min.   :        1       Min.   :  0.000               
##  1st Qu.:    47030   1st Qu.:    23380       1st Qu.:  0.728               
##  Median :   334959   Median :   189671       Median :  3.720               
##  Mean   :  6102907   Mean   :  2860705       Mean   : 10.048               
##  3rd Qu.:  2046726   3rd Qu.:  1008912       3rd Qu.: 11.412               
##  Max.   :327158278   Max.   :126779833       Max.   :175.270               
##  NA's   :12606       NA's   :14295           NA's   :12032                 
##  people_vaccinated_per_hundred people_fully_vaccinated_per_hundred
##  Min.   : 0.000                Min.   : 0.000                     
##  1st Qu.: 0.640                1st Qu.: 0.320                     
##  Median : 3.050                Median : 1.390                     
##  Mean   : 7.424                Mean   : 3.562                     
##  3rd Qu.: 8.610                3rd Qu.: 3.350                     
##  Max.   :92.300                Max.   :82.970                     
##  NA's   :12606                 NA's   :14295

3.4 NA Values

The summary shows that we now have NA values in all of our numeric columns, and the table of data shows that there are also values in the iso_code column.

3.4.1 ISO Code

When we completed the missing dates, the corresponding ISO Codes were not filled in. We can add these by getting a data frame with each location and ISO code (from the original data), then joining that to the complete_vacc_data data

iso_codes <- vaccination_data %>%
  distinct(location, iso_code)
complete_vacc_data %<>% select(-iso_code)
complete_vacc_data <- left_join(complete_vacc_data, iso_codes, 
                                 by = 'location')  
complete_vacc_data

3.4.2 Total Vaccination Numbers

The vaccination figures left in our data are running totals, which means that we would not expect them to decrease. We won’t make any assumptions about how many vaccinations were performed on any days where there is no data, but will use the tidyr fill function to copy totals down from the last day with data:

complete_vacc_data %<>% 
  arrange(location, date) %>%
  group_by(location) %>%
  fill(c(total_vaccinations, people_vaccinated, people_fully_vaccinated,
         total_vaccinations_per_hundred, people_vaccinated_per_hundred,
         people_fully_vaccinated_per_hundred, people_fully_vaccinated_per_hundred))
complete_vacc_data

We now have totals for every country once they report their first figures, but before that the values will be NA. Again, we won’t make any assumptions about any vaccinations before the first report, so we’ll set these values to 0.

complete_vacc_data[is.na(complete_vacc_data)] = 0
complete_vacc_data

3.5 Removing Data Which is Not For Sovereign Nations

Some of our data is not for sovereign nations which mean they don’t have valid ISO Codes. We’ll store that data separately before removing it from our main data frame:

non_nation_data <- complete_vacc_data %>%
  filter(!nchar(iso_code) == 3)
non_nation_data
non_nation_data %>% distinct(iso_code)
continent_vacc_data <- non_nation_data %>%
  filter(location %in% c('Africa', 'Asia', 'Europe', 'North America', 'Oceania', 'South America'))

continent_vacc_data
national_vacc_data <- complete_vacc_data %>%
  filter(nchar(iso_code) == 3)

national_vacc_data

We now have 3 data frames that we may want to use for reporting and visualising our data:

  • complete_vacc_data - data for all countries and regions
  • national_vacc_data - data for all sovereign nations with ISO 3166-1 codes
  • continent_vacc_data - summary data for continents

3.6 Converting character columns to factors

We will complete the data cleaning process by converting the 2 string columns to factors. We’ll do this for all 3 data frames:

complete_vacc_data %<>%
  mutate(location = as.factor(location)) %>%
  mutate(iso_code = as.factor(iso_code))
national_vacc_data %<>%
  mutate(location = as.factor(location)) %>%
  mutate(iso_code = as.factor(iso_code))
continent_vacc_data %<>%
  mutate(location = as.factor(location)) %>%
  mutate(iso_code = as.factor(iso_code))

4 Further Information

Description of the Our World in Data COVID-19 Vaccination data: https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations